{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes:\n",
    " * same as 6-2 but with no TOI data in validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/1/AstroCNNModelVetting_vrevised_tuned_20211023_194357',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/2/AstroCNNModelVetting_vrevised_tuned_20211023_200854',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/3/AstroCNNModelVetting_vrevised_tuned_20211023_203347',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/4/AstroCNNModelVetting_vrevised_tuned_20211023_205843',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/5/AstroCNNModelVetting_vrevised_tuned_20211023_212345',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/6/AstroCNNModelVetting_vrevised_tuned_20211023_214902',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/7/AstroCNNModelVetting_vrevised_tuned_20211023_221411',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/8/AstroCNNModelVetting_vrevised_tuned_20211023_223911',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/9/AstroCNNModelVetting_vrevised_tuned_20211023_230410',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2/10/AstroCNNModelVetting_vrevised_tuned_20211023_232907']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "chkpt_root = '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_run_2'\n",
    "data_files = '/mnt/tess/astronet/tfrecords-vetting-6-val/*'\n",
    "tces_file = '/mnt/tess/astronet/tces-vetting-v5-val.csv'\n",
    "\n",
    "nruns = 10\n",
    "\n",
    "def load_ensemble(chkpt_root, nruns):\n",
    "    checkpts = []\n",
    "    for i in range(nruns):\n",
    "        parent = os.path.join(chkpt_root, str(i + 1))\n",
    "        if not os.path.exists(parent):\n",
    "            break\n",
    "        all_dirs = os.listdir(parent)\n",
    "        if not all_dirs:\n",
    "            break\n",
    "        d, = all_dirs\n",
    "        checkpts.append(os.path.join(parent, d))\n",
    "    return checkpts\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model 1\n",
      "231 records\n",
      "Running model 2\n",
      "231 records\n",
      "Running model 3\n",
      "231 records\n",
      "Running model 4\n",
      "231 records\n",
      "Running model 5\n",
      "231 records\n",
      "Running model 6\n",
      "231 records\n",
      "Running model 7\n",
      "231 records\n",
      "Running model 8\n",
      "231 records\n",
      "Running model 9\n",
      "231 records\n",
      "Running model 10\n",
      "231 records\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "from astronet import predict\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def run_predictions(path):\n",
    "    predict.FLAGS = predict.parser.parse_args([\n",
    "      '--model_dir', path,\n",
    "      '--data_files', data_files,\n",
    "      '--output_file', '',\n",
    "    ])\n",
    "\n",
    "    return predict.predict()\n",
    "\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "ensemble_preds = []\n",
    "config = None\n",
    "for i, path in enumerate(paths):\n",
    "    print(f'Running model {i + 1}')\n",
    "    preds, config = run_predictions(path)\n",
    "    ensemble_preds.append(preds.set_index('tic_id'))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['disp_p', 'disp_e', 'disp_n']\n",
    "\n",
    "col_p = labels.index('disp_p')\n",
    "thresh = 0.1579816417694639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "agg_preds = {}\n",
    "\n",
    "for preds in ensemble_preds:\n",
    "    for tic_id in preds.index:\n",
    "        if tic_id not in agg_preds:\n",
    "            agg_preds[tic_id] = []\n",
    "\n",
    "        row = preds[preds.index == tic_id]\n",
    "        pred_v = row.values[0]\n",
    "        if len(row.values) > 1:\n",
    "            print(f'Warning: duplicate predictions for {tic_id}')\n",
    "        if pred_v[col_p] >= thresh:\n",
    "            agg_preds[tic_id].append('disp_p')\n",
    "        else:\n",
    "            agg_preds[tic_id].append(preds.columns[np.argmax(pred_v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_preds = []\n",
    "for tic_id in list(agg_preds.keys()):\n",
    "    counts = {l: 0 for l in labels}\n",
    "    for e in agg_preds[tic_id]:\n",
    "        counts[e] += 1\n",
    "    maxcount = max(counts.values())\n",
    "    counts.update({\n",
    "        'tic_id': tic_id,\n",
    "        'maxcount': maxcount,\n",
    "    })\n",
    "    final_preds.append(counts)\n",
    "    \n",
    "final_preds = pd.DataFrame(final_preds).set_index('tic_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tce_table = pd.read_csv(tces_file, header=0).set_index('tic_id')\n",
    "tce_labels = tce_table[labels]\n",
    "\n",
    "pl = final_preds.join(tce_labels, on='tic_id', how='left', lsuffix='_p')\n",
    "\n",
    "pl.head()\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 1.0\n",
      "Precision: 0.30158730158730157\n"
     ]
    }
   ],
   "source": [
    "ppos = (pl['disp_p_p'] > 0)\n",
    "pos = (pl['disp_p'] > 0)\n",
    "\n",
    "pneg = (pl['disp_p_p'] == 0)\n",
    "neg = (pl['disp_p'] == 0)\n",
    "\n",
    "print('Recall:', len(pl[ppos & pos]) / len(pl[pos]))\n",
    "print('Precision:', len(pl[ppos & pos]) / len(pl[ppos]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in pl[pos & pneg][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113594232\n",
      "118837152\n",
      "119774374\n",
      "23826331\n",
      "24491836\n",
      "271903247\n",
      "276645751\n",
      "276936320\n",
      "278225267\n",
      "278866211\n",
      "280095254\n",
      "30275619\n",
      "304729846\n",
      "307153959\n",
      "344978642\n",
      "372222115\n",
      "380783252\n",
      "384068226\n",
      "384744828\n",
      "424876542\n",
      "469782185\n",
      "61802871\n",
      "74934381\n",
      "98350979\n",
      "142963071\n",
      "137778682\n",
      "365494323\n",
      "388076435\n",
      "149854222\n",
      "326974824\n",
      "2234723\n",
      "262173985\n",
      "275770230\n",
      "28016860\n",
      "321828403\n",
      "723801895\n",
      "24004619\n",
      "457138169\n",
      "365203184\n",
      "255994336\n",
      "1715469662\n",
      "1715469667\n",
      "229948921\n",
      "272783172\n"
     ]
    }
   ],
   "source": [
    "for i in pl[neg & ppos][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.991785</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.001995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.981941</td>\n",
       "      <td>0.016628</td>\n",
       "      <td>0.001431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.987828</td>\n",
       "      <td>0.010332</td>\n",
       "      <td>0.001840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.975331</td>\n",
       "      <td>0.021479</td>\n",
       "      <td>0.003191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.991088</td>\n",
       "      <td>0.006859</td>\n",
       "      <td>0.002053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.993468</td>\n",
       "      <td>0.005518</td>\n",
       "      <td>0.001014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.995082</td>\n",
       "      <td>0.003815</td>\n",
       "      <td>0.001103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.978302</td>\n",
       "      <td>0.018916</td>\n",
       "      <td>0.002782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.994730</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.001100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.995018</td>\n",
       "      <td>0.003359</td>\n",
       "      <td>0.001623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             disp_p    disp_e    disp_n\n",
       "tic_id                                 \n",
       "113428669  0.991785  0.006221  0.001995\n",
       "113428669  0.981941  0.016628  0.001431\n",
       "113428669  0.987828  0.010332  0.001840\n",
       "113428669  0.975331  0.021479  0.003191\n",
       "113428669  0.991088  0.006859  0.002053\n",
       "113428669  0.993468  0.005518  0.001014\n",
       "113428669  0.995082  0.003815  0.001103\n",
       "113428669  0.978302  0.018916  0.002782\n",
       "113428669  0.994730  0.004170  0.001100\n",
       "113428669  0.995018  0.003359  0.001623"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare(ensemble_preds, filter):\n",
    "    result = ensemble_preds[0][filter]\n",
    "    for preds in ensemble_preds[1:]:\n",
    "        result = result.append(preds[filter])\n",
    "    return result\n",
    "\n",
    "compare(ensemble_preds, preds.index == 113428669)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p_p</th>\n",
       "      <th>disp_e_p</th>\n",
       "      <th>disp_n_p</th>\n",
       "      <th>maxcount</th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           disp_p_p  disp_e_p  disp_n_p  maxcount  disp_p  disp_e  disp_n\n",
       "tic_id                                                                   \n",
       "113428669        10         0         0        10       1       0       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[pl.index == 113428669]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tics = set(ensemble_preds[0].index.values)\n",
    "\n",
    "tic_index = {v: i for i, v in enumerate(tics)}\n",
    "\n",
    "pred_ps = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_es = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_ns = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "for i, preds in enumerate(ensemble_preds):\n",
    "    for row in preds.iterrows():\n",
    "        tic, pred_p = row[0], row[1][col_p]\n",
    "        pred_ps[i][tic_index[tic]] = pred_p\n",
    "        pred_es[i][tic_index[tic]] = row[1][labels.index('disp_e')]\n",
    "        pred_ns[i][tic_index[tic]] = row[1][labels.index('disp_n')]\n",
    "\n",
    "lbl_ps = np.zeros([len(tic_index)], dtype=np.bool)\n",
    "bad_labels = []\n",
    "for row in tce_labels.iterrows():\n",
    "    tic, lbl_p = row[0], row[1]['disp_p']\n",
    "    if tic in tic_index:\n",
    "        lbl_ps[tic_index[tic]] = (lbl_p > 0)\n",
    "    else:\n",
    "        bad_labels.append(tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cond_pos = float(np.sum(lbl_ps))\n",
    "\n",
    "def pr_at_th(th):\n",
    "    pred_pos = np.any(pred_ps >= th, axis=0)\n",
    "    true_pos = pred_pos & lbl_ps\n",
    "    num_pred_pos = int(np.sum(pred_pos))\n",
    "    num_true_pos = int(np.sum(true_pos))\n",
    "    if num_pred_pos == 0:\n",
    "        return 1.0, 0.0\n",
    "    return float(num_true_pos) / float(num_pred_pos), float(num_true_pos) / float(num_cond_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.6049754909500172, max R: 1.0, max P: 1.0\n",
      "100% recall at: 30%, threshold: 0.15807711791997647\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x740 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "ps, rs, ths = ([], [], [])\n",
    "th = np.max(pred_ps)\n",
    "while th >= 0.0:\n",
    "    p, r = pr_at_th(th)\n",
    "    if p > 0:\n",
    "        ps.append(p)\n",
    "        rs.append(r)\n",
    "    else:\n",
    "        ps.append(1.0)\n",
    "        rs.append(0.0)\n",
    "    ths.append(th)\n",
    "    th -= 0.0005\n",
    "    \n",
    "from sklearn import metrics\n",
    "\n",
    "print(f'AUC: {metrics.auc(rs, ps)}, max R: {max(rs)}, max P: {max(ps)}')\n",
    "    \n",
    "i = len(rs) - 1\n",
    "while rs[i] == 1.0:\n",
    "    i -= 1\n",
    "i += 1\n",
    "print(f'100% recall at: {int(ps[i] * 100)}%, threshold: {ths[i]}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3.7), dpi=200)\n",
    "\n",
    "ax.spines['top'].set_color('#808080')\n",
    "ax.spines['right'].set_color('#808080')\n",
    "ax.spines['left'].set_color('#808080')\n",
    "ax.spines['bottom'].set_color('#808080')\n",
    "ax.tick_params(direction='in', color='#808080')\n",
    "\n",
    "plt.grid(color='#c0c0c0', linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.ylabel('Precision', fontweight='bold')\n",
    "plt.xlabel('Recall', fontweight='bold')\n",
    "\n",
    "plt.xlim((0.0, 1.0))\n",
    "plt.ylim((0.0, 1.0))\n",
    "\n",
    "_ = plt.plot(rs, ps, marker='.')\n",
    "_ = plt.plot([0.0, 1.0], [1.0, 0.0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
